import torch
import torch.nn as nn
from torch.optim import Adam
from torch.utils.data import DataLoader
from diffusers import UNet2DModel, DDPMScheduler
from tqdm import tqdm

# 定义模型 (扩散模型 U-Net)
class ConditionalUNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.unet = UNet2DModel(
            sample_size=64,           # 示例输入图像尺寸 (H, W)
            in_channels=4,            # 输入通道数为 1+3=4 (目标 1 通道 + 条件图像 3 通道)
            out_channels=1,           # 输出图像为 1 通道
            layers_per_block=2,
            block_out_channels=(64, 128, 256, 512),
            down_block_types=(
                "DownBlock2D", "DownBlock2D", "DownBlock2D", "AttnDownBlock2D"
            ),
            up_block_types=(
                "AttnUpBlock2D", "UpBlock2D", "UpBlock2D", "UpBlock2D"
            ),
        )

    def forward(self, x, condition):
        # 在通道维度 dim=1 上拼接目标和条件图像
        x = torch.cat([x, condition], dim=1)
        return self.unet(x).sample

# 定义扩散模型流程
class DiffusionPipeline:
    def __init__(self, model, scheduler):
        self.model = model
        self.scheduler = scheduler

    def train(self, dataloader, epochs, optimizer, device):
        self.model.train()
        loss_fn = nn.MSELoss()
        
        for epoch in range(epochs):
            epoch_loss = 0
            for target, condition in tqdm(dataloader, desc=f"Epoch {epoch + 1}"):
                target, condition = target.to(device), condition.to(device)

                # 1. 采样时间步数
                timesteps = torch.randint(0, self.scheduler.config.num_train_timesteps, (target.size(0),), device=device)

                # 2. 对目标添加噪声
                noisy_target = self.scheduler.add_noise(target, torch.randn_like(target), timesteps)

                # 3. 模型预测噪声
                predicted_noise = self.model(noisy_target, condition)

                # 4. 计算损失
                loss = loss_fn(predicted_noise, torch.randn_like(target))
                optimizer.zero_grad()
                loss.backward()
                optimizer.step()

                epoch_loss += loss.item()

            print(f"Epoch {epoch + 1} Loss: {epoch_loss / len(dataloader)}")

    def sample(self, condition, num_inference_steps, device):
        self.model.eval()
        condition = condition.to(device)
        
        with torch.no_grad():
            # 初始化为随机噪声
            generated = torch.randn((condition.size(0), 1, condition.size(2), condition.size(3)), device=device)
            
            for t in tqdm(reversed(range(num_inference_steps)), desc="Sampling"):
                t_tensor = torch.full((generated.size(0),), t, device=device, dtype=torch.long)
                generated = self.scheduler.step(self.model(generated, condition), t_tensor, generated).prev_sample

        return generated

# 数据加载器 (留空部分，根据实际数据集进行实现)
class CustomDataset(torch.utils.data.Dataset):
    def __init__(self):
        # 在此处加载并预处理数据
        pass

    def __len__(self):
        # 返回数据集大小
        return 0

    def __getitem__(self, idx):
        # 返回 (目标图像, 条件图像) 二元组
        return torch.zeros(1, 64, 64), torch.zeros(3, 64, 64)

# 主函数
if __name__ == "__main__":
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    # 初始化模型、优化器和调度器
    model = ConditionalUNet().to(device)
    scheduler = DDPMScheduler(num_train_timesteps=1000)
    optimizer = Adam(model.parameters(), lr=1e-4)

    # 加载数据
    dataset = CustomDataset()
    dataloader = DataLoader(dataset, batch_size=16, shuffle=True)

    # 创建扩散管道
    pipeline = DiffusionPipeline(model, scheduler)

    # 训练模型
    pipeline.train(dataloader, epochs=10, optimizer=optimizer, device=device)

    # 采样测试
    test_condition = torch.zeros(4, 3, 64, 64)  # 假设的测试条件图像
    generated_images = pipeline.sample(test_condition, num_inference_steps=1000, device=device)

    # 保存生成结果
    torch.save(generated_images, "generated_images.pt")
